Genome-wide association studies using machine learning

Genom-Wide Association Study (GWAS) is a field that studies the genomics of organisms. GWAS focuses on what is called single nucleotide polymorphisms (SNP). In GWAS,the data of the SNPs is usually massive, which requires modern statistical analysis methods to investigate the relationship between those SNPs and a phenotype. Analyzing the SNP data is difficult and time-consuming. Machine learning (ML) is a standard solution to investigate the relationship between data with many variables and features. Many ML methods are used to analyze the SNP data, but what determines which method is the best for this task. Elastic Net regression, LASSO regression, and Deep Learning are ML methods appropriate for this task. Depending on the SNP data, one of these three methods has to be more useful for this task. In addition, understanding the SNP data can affect understanding of the mutations between the organisms of the same population, leading to economic and ecological advantages. The Elastic Net model showed the lowest root meansquare error compared to LASSO regression and Deep Learning models.Pag